Characteristics of typical signals, disturbances, noise and interference including myoelectric (EMG) signals, cardiac (ECG) signals, movement-induced disturbances, background noise, and interference from the mains will be first reviewed.
Myoelectric (EMG) Signal
An EMG signal reflects the nervous activation of a muscle or a group of muscles, and presents the character of a band-limited noise in extra-muscular readings. The strength and spectral contents of an EMG signal are determined by the following parameters:                (1) the number of motor units (muscles) recruited;        (2) the repetition rate of motor unit activation;        (3) the distance between the source (fibers of the muscle(s)) and the electrode(s) used to sense the EMG signal;        (4) the width of the innervation zone;        (5) the propagation velocity of the activation potentials;        (6) the electrode configuration including, for example, the distance between electrode plates, the parallel or perpendicular alignment of the electrode plates with respect to the direction of the fibers of the muscle(s), etc.; and        (7) other parameters such as the length of the fibers of the muscle(s), the position of the electrode(s) with respect to both the innervation zone and the ends of the fibers of the muscle(s), etc.        
For many muscles, except very short muscles, the above-mentioned parameters produce a spectrum whose amplitude increases from zero at zero frequency (Direct Current (DC)), presents a peak in the region of 40–100 Hz and, then, decreases gradually as the frequency increases.
Models that take into consideration the above listed parameters show that these parameters are multiplicative in the frequency domain, indicating that the time dependent signals are multiple convolutions of the phenomena associated with these parameters. The time representation of an EMG signal is thus very complex, and most attempts to process such a signal has consequently involved filtering of the EMG signal through certain frequency-filtering characteristics.
Electrocardiogram (ECG) Signal
An ECG signal is generated from electrical activity of the heart, which is rather coherent in space and time. Thus, an ECG signal has a deterministic character in the time domain except under certain pathological conditions such as fibrillation. In the frequency domain, maximum density of the spectral energy is found in the region around 10–15 Hz with the spectral energy density decreasing as the frequency increases.
Since contributions to the ECG signal are synchronized, the ECG signal has a much higher energy than an EMG signal. Moreover, significant ECG contributions are located in the frequency bandwidth where the density of energy of an EMG signal is maximal, whereby ECG is likely to disturb an EMG signal.
Movement Induced Disturbance
Movement of the electrodes cause variations in the electrode contact resistance and electrode contact capacitance, which modulate the (half-battery) voltage across the metal-tissue junction to cause a corresponding disturbance. This disturbance, often referred to as “varying baseline”, has high-amplitude low-frequency spectral components since it is caused by a slow mechanical movement. The frequency characteristic of this disturbance is often lower in frequency than the frequency characteristic associated with the time window of the signal epoch and, thus, this disturbance manifests itself as a large offset DC component.
It is also observed that variations in the baseline are equivalent to variations in the spectral components above DC (zero frequency). Therefore, the large potential behind baseline variations (magnitude of the order of, for example, one volt) also makes the spectrum density of the variation significant.
Background Noise
In addition to the above-mentioned signal contributions, there is a noise component. This noise component has a rather unspecific character and originates from a number of different sources. It is often modelled as a random component with constant (or slowly varying) spectral density in the whole frequency bandwidth of interest.
Interference from the Mains
Interference from the mains has a well-defined signal shape with a fixed frequency. Unknown parameters of this interference are the phase and the amplitude, which may vary considerably.
Currently Used Filter Techniques to Separate Signal Contributions
In the following description, some of the problems associated with the separation of signal components such as EMG, ECG, baseline variations, and noise will be discussed. Drawbacks associated with currently used signal filtering methods will also be considered.
EMG Signal vs. Movement Induced Disturbance
Although rather separated in frequency, the high energy density of the movement induced disturbances becomes significant also in the frequency bandwidth where EMG signals have a maximum amplitude. Recursive filtering is often used to remove movement induced disturbances from EMG signals. However, recursive filtering presents the drawback of suffering from the long time it takes to forget the high energy input from a sudden baseline variation. For that reason, recursive filtering should be avoided.
EMG Signal vs. Background Noise
The spectra of EMG signal and background noise overlap and both have a character of random noise. Optimum (Wiener) filtering can be used, but a low-pass filter cutting off the high frequency portion of the noise can be sufficient since this will remove the major portion of the background noise.
EMG Signal vs. ECG Signal
As indicated in the foregoing description, EMG and ECG signals have partly overlapping power spectra. Strong ECG signals create significant spectral energy density at frequencies where energy peaks of the EMG signals appear. Efficient filtering to remove an ECG signal from an EMG signal or vice versa should use the deterministic character of the ECG signal in the time domain.
ECG Signal vs. Movement Induced Disturbance
ECG signals and movement induced disturbances have respective spectra with a considerable overlap and are difficult to separate in the frequency domain. The deterministic character of ECG in the time domain is often utilized, in particular for averaging QRS complexes. A drawback of such filtering is that it introduces in the output from the filter a delay having a duration as long as ten (10) seconds.
ECG Signal vs. Background Noise
Although ECG signals and background noise overlap in the low frequency region of the spectrum, the ECG signal is mostly dominating and, therefore, a low-pass filtering is normally sufficient to remove most of the background noise from an ECG recording.
Movement Induced Disturbance vs. Background Noise
Although this case is of interest in quality control of the signal, this issue will not be further elaborated in the present specification.
Interference from the Mains and Power Line Interference
A common technique for removing an interference from the mains and a power line interference is to process the disturbed signal through a notch filter which is quite sharp at the particular frequency of these interferences. This solution presents the drawback that a notch filter introduces large phase shifts in the remaining part of the signal. As mentioned hereinabove, the nature of these interferences is “a priori” well known, and their frequency is mostly constant. Only the start instant within the oscillation, the phase, and the amplitude are unknown and have to be calculated. This “a priori” knowledge implies that single periods or even portions of the interference oscillation can be used to perform the latter calculation.
From the above-described characteristics of the various signal contributions, the following acknowledgments can be made:                Filtering in the frequency domain requires very steep filters to extract the EMG signal and to suppress the ECG signal and the interference from the mains. Steep filters present the drawback of being associated with large phase shifts and ringing.        Since the baseline variations are large and slow, recursive filtering should be avoided since these filters have a memory of past events.        Filtering should occur over finite epoch lengths, and Infinite Impulse Response (IIR) filters should not be used.        Filters designed from a frequency-domain point of view present the drawbacks of having 1) slow convergence since the trigonometric series are not well suited to describe baseline variations, 2) remaining memory of earlier events due to their (often) recursive character, and 3) large phase shifts and ringing in proportion of their sharpness.        
In view of the above, a new filtering technique is needed to efficiently isolate or remove one or many of the above discussed signal contributions from an input signal.